Overview

Dataset statistics

Number of variables20
Number of observations7146
Missing cells8168
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory491.1 B

Variable types

Numeric13
Categorical7

Alerts

Address has a high cardinality: 7055 distinct valuesHigh cardinality
CondoProject has a high cardinality: 202 distinct valuesHigh cardinality
Style has a high cardinality: 81 distinct valuesHigh cardinality
Sale_date has a high cardinality: 313 distinct valuesHigh cardinality
PropertyID is highly overall correlated with taxkey and 2 other fieldsHigh correlation
taxkey is highly overall correlated with PropertyID and 2 other fieldsHigh correlation
District is highly overall correlated with PropertyID and 1 other fieldsHigh correlation
nbhd is highly overall correlated with PropertyID and 3 other fieldsHigh correlation
Stories is highly overall correlated with FinishedSqft and 2 other fieldsHigh correlation
Year_Built is highly overall correlated with Style and 1 other fieldsHigh correlation
Rooms is highly overall correlated with FinishedSqft and 2 other fieldsHigh correlation
FinishedSqft is highly overall correlated with Stories and 4 other fieldsHigh correlation
Units is highly overall correlated with Stories and 3 other fieldsHigh correlation
Bdrms is highly overall correlated with Rooms and 1 other fieldsHigh correlation
Sale_price is highly overall correlated with StyleHigh correlation
PropType is highly overall correlated with nbhd and 1 other fieldsHigh correlation
Style is highly overall correlated with nbhd and 6 other fieldsHigh correlation
Extwall is highly overall correlated with Year_BuiltHigh correlation
PropType is highly imbalanced (62.8%)Imbalance
Hbath is highly imbalanced (51.5%)Imbalance
CondoProject has 6261 (87.6%) missing valuesMissing
Extwall has 926 (13.0%) missing valuesMissing
Rooms has 443 (6.2%) missing valuesMissing
Bdrms has 443 (6.2%) missing valuesMissing
Units is highly skewed (γ1 = 42.80622276)Skewed
Lotsize is highly skewed (γ1 = 33.88617801)Skewed
Address is uniformly distributedUniform
Rooms has 122 (1.7%) zerosZeros
Fbath has 509 (7.1%) zerosZeros
Lotsize has 489 (6.8%) zerosZeros

Reproduction

Analysis started2024-03-01 09:48:42.108116
Analysis finished2024-03-01 09:49:32.299344
Duration50.19 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

PropertyID
Real number (ℝ)

Distinct7055
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178756.77
Minimum98461
Maximum266040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:32.668068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum98461
5-th percentile105790.25
Q1136233.25
median176670.5
Q3221564.25
95-th percentile253672.25
Maximum266040
Range167579
Interquartile range (IQR)85331

Descriptive statistics

Standard deviation47982.982
Coefficient of variation (CV)0.26842609
Kurtosis-1.2318058
Mean178756.77
Median Absolute Deviation (MAD)42517
Skewness0.059076039
Sum1.2773959 × 109
Variance2.3023665 × 109
MonotonicityNot monotonic
2024-03-01T10:49:33.257198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176566 2
 
< 0.1%
183128 2
 
< 0.1%
236196 2
 
< 0.1%
214075 2
 
< 0.1%
172832 2
 
< 0.1%
141917 2
 
< 0.1%
114543 2
 
< 0.1%
141962 2
 
< 0.1%
213578 2
 
< 0.1%
213568 2
 
< 0.1%
Other values (7045) 7126
99.7%
ValueCountFrequency (%)
98461 1
< 0.1%
98464 1
< 0.1%
98508 1
< 0.1%
98519 1
< 0.1%
98561 1
< 0.1%
98593 1
< 0.1%
98604 1
< 0.1%
98608 1
< 0.1%
98696 1
< 0.1%
98715 1
< 0.1%
ValueCountFrequency (%)
266040 1
< 0.1%
266025 1
< 0.1%
266017 1
< 0.1%
266009 1
< 0.1%
265996 1
< 0.1%
265962 1
< 0.1%
265958 1
< 0.1%
265953 1
< 0.1%
265945 1
< 0.1%
265842 1
< 0.1%

PropType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size474.7 KiB
Residential
5774 
Condominium
887 
Commercial
 
240
Lg Apartment
 
238
Manufacturing
 
6

Length

Max length13
Median length11
Mean length11.0007
Min length6

Characters and Unicode

Total characters78611
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowManufacturing
2nd rowCommercial
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Residential 5774
80.8%
Condominium 887
 
12.4%
Commercial 240
 
3.4%
Lg Apartment 238
 
3.3%
Manufacturing 6
 
0.1%
Exempt 1
 
< 0.1%

Length

2024-03-01T10:49:33.773717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T10:49:34.397653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
residential 5774
78.2%
condominium 887
 
12.0%
commercial 240
 
3.3%
lg 238
 
3.2%
apartment 238
 
3.2%
manufacturing 6
 
0.1%
exempt 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 13568
17.3%
e 12027
15.3%
n 7798
9.9%
d 6661
8.5%
a 6264
8.0%
t 6257
8.0%
l 6014
7.7%
R 5774
7.3%
s 5774
7.3%
m 2493
 
3.2%
Other values (14) 5981
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70989
90.3%
Uppercase Letter 7384
 
9.4%
Space Separator 238
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 13568
19.1%
e 12027
16.9%
n 7798
11.0%
d 6661
9.4%
a 6264
8.8%
t 6257
8.8%
l 6014
8.5%
s 5774
8.1%
m 2493
 
3.5%
o 2014
 
2.8%
Other values (7) 2119
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
R 5774
78.2%
C 1127
 
15.3%
L 238
 
3.2%
A 238
 
3.2%
M 6
 
0.1%
E 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78373
99.7%
Common 238
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 13568
17.3%
e 12027
15.3%
n 7798
9.9%
d 6661
8.5%
a 6264
8.0%
t 6257
8.0%
l 6014
7.7%
R 5774
7.4%
s 5774
7.4%
m 2493
 
3.2%
Other values (13) 5743
7.3%
Common
ValueCountFrequency (%)
238
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 13568
17.3%
e 12027
15.3%
n 7798
9.9%
d 6661
8.5%
a 6264
8.0%
t 6257
8.0%
l 6014
7.7%
R 5774
7.3%
s 5774
7.3%
m 2493
 
3.2%
Other values (14) 5981
7.6%

taxkey
Real number (ℝ)

Distinct7055
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4687434 × 109
Minimum30131000
Maximum7.160375 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:35.004597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum30131000
5-th percentile1.1701463 × 109
Q12.3110052 × 109
median3.211215 × 109
Q34.703136 × 109
95-th percentile5.8008152 × 109
Maximum7.160375 × 109
Range7.130244 × 109
Interquartile range (IQR)2.3921308 × 109

Descriptive statistics

Standard deviation1.4845672 × 109
Coefficient of variation (CV)0.42798416
Kurtosis-0.69460439
Mean3.4687434 × 109
Median Absolute Deviation (MAD)1.0810275 × 109
Skewness0.155261
Sum2.478764 × 1013
Variance2.2039399 × 1018
MonotonicityIncreasing
2024-03-01T10:49:35.620003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3211565100 2
 
< 0.1%
3460532000 2
 
< 0.1%
5290638000 2
 
< 0.1%
4591388000 2
 
< 0.1%
3141167100 2
 
< 0.1%
2550046000 2
 
< 0.1%
1730015000 2
 
< 0.1%
2550103000 2
 
< 0.1%
4590325000 2
 
< 0.1%
4590315000 2
 
< 0.1%
Other values (7045) 7126
99.7%
ValueCountFrequency (%)
30131000 1
< 0.1%
30152000 1
< 0.1%
49980110 1
< 0.1%
49993200 1
< 0.1%
50042000 1
< 0.1%
50074000 1
< 0.1%
50085000 1
< 0.1%
50089000 1
< 0.1%
70017000 1
< 0.1%
70036000 1
< 0.1%
ValueCountFrequency (%)
7160375000 1
< 0.1%
7160366000 1
< 0.1%
7160365000 1
< 0.1%
7160351000 1
< 0.1%
7160339000 1
< 0.1%
7160327000 1
< 0.1%
7160283000 1
< 0.1%
7160279000 1
< 0.1%
7160254000 1
< 0.1%
7160241000 1
< 0.1%

Address
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct7055
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size517.2 KiB
2535-2537 N BUFFUM ST
 
2
2153 N 54TH ST
 
2
3322 S 67TH ST
 
2
2522 W LAPHAM ST
 
2
3072 N RICHARDS ST
 
2
Other values (7050)
7136 

Length

Max length37
Median length31
Mean length17.094318
Min length12

Characters and Unicode

Total characters122156
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6964 ?
Unique (%)97.5%

Sample

1st row9434-9446 N 107TH ST
2nd row9306-9316 N 107TH ST
3rd row9327 N SWAN RD
4th row9411 W COUNTY LINE RD
5th row9322 N JOYCE AV

Common Values

ValueCountFrequency (%)
2535-2537 N BUFFUM ST 2
 
< 0.1%
2153 N 54TH ST 2
 
< 0.1%
3322 S 67TH ST 2
 
< 0.1%
2522 W LAPHAM ST 2
 
< 0.1%
3072 N RICHARDS ST 2
 
< 0.1%
10226 W CAPITOL DR 2
 
< 0.1%
5612 W CARMEN AV 2
 
< 0.1%
4215 N 100TH ST, Unit 148 2
 
< 0.1%
2529 W MAPLE ST 2
 
< 0.1%
1718-1720 S 26TH ST 2
 
< 0.1%
Other values (7045) 7126
99.7%

Length

2024-03-01T10:49:36.193503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 4604
 
15.2%
n 3433
 
11.3%
w 1699
 
5.6%
av 1661
 
5.5%
s 1596
 
5.3%
unit 771
 
2.5%
e 431
 
1.4%
pl 285
 
0.9%
dr 180
 
0.6%
rd 122
 
0.4%
Other values (5383) 15604
51.4%

Most occurring characters

ValueCountFrequency (%)
23240
19.0%
T 9224
 
7.6%
S 7467
 
6.1%
2 5877
 
4.8%
1 5623
 
4.6%
N 5617
 
4.6%
3 5300
 
4.3%
4 4239
 
3.5%
5 4130
 
3.4%
0 4084
 
3.3%
Other values (45) 47355
38.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 53509
43.8%
Decimal Number 41100
33.6%
Space Separator 23240
19.0%
Lowercase Letter 2337
 
1.9%
Dash Punctuation 1196
 
1.0%
Other Punctuation 774
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 9224
17.2%
S 7467
14.0%
N 5617
10.5%
A 3975
 
7.4%
H 3731
 
7.0%
E 3127
 
5.8%
R 2733
 
5.1%
W 2303
 
4.3%
L 2210
 
4.1%
V 1972
 
3.7%
Other values (16) 11150
20.8%
Lowercase Letter
ValueCountFrequency (%)
t 771
33.0%
n 771
33.0%
i 771
33.0%
d 6
 
0.3%
f 4
 
0.2%
j 3
 
0.1%
b 2
 
0.1%
k 2
 
0.1%
a 2
 
0.1%
m 1
 
< 0.1%
Other values (4) 4
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 5877
14.3%
1 5623
13.7%
3 5300
12.9%
4 4239
10.3%
5 4130
10.0%
0 4084
9.9%
6 3307
8.0%
7 3085
7.5%
8 2863
7.0%
9 2592
6.3%
Other Punctuation
ValueCountFrequency (%)
, 771
99.6%
# 2
 
0.3%
\ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
23240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66310
54.3%
Latin 55846
45.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 9224
16.5%
S 7467
13.4%
N 5617
10.1%
A 3975
 
7.1%
H 3731
 
6.7%
E 3127
 
5.6%
R 2733
 
4.9%
W 2303
 
4.1%
L 2210
 
4.0%
V 1972
 
3.5%
Other values (30) 13487
24.2%
Common
ValueCountFrequency (%)
23240
35.0%
2 5877
 
8.9%
1 5623
 
8.5%
3 5300
 
8.0%
4 4239
 
6.4%
5 4130
 
6.2%
0 4084
 
6.2%
6 3307
 
5.0%
7 3085
 
4.7%
8 2863
 
4.3%
Other values (5) 4562
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23240
19.0%
T 9224
 
7.6%
S 7467
 
6.1%
2 5877
 
4.8%
1 5623
 
4.6%
N 5617
 
4.6%
3 5300
 
4.3%
4 4239
 
3.5%
5 4130
 
3.4%
0 4084
 
3.3%
Other values (45) 47355
38.8%

CondoProject
Categorical

HIGH CARDINALITY  MISSING 

Distinct202
Distinct (%)22.8%
Missing6261
Missing (%)87.6%
Memory size260.0 KiB
LANDMARK ON THE LAKE
 
27
WOODLANDS
 
26
BLATZ
 
23
MILL VALLEY
 
20
POINT ON THE RIVER CONDOS
 
19
Other values (197)
770 

Length

Max length35
Median length26
Mean length17.267797
Min length5

Characters and Unicode

Total characters15282
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)8.1%

Sample

1st rowNORTHRIDGE WOOD LAKE
2nd rowNORTHRIDGE WOOD LAKE
3rd rowNORTHRIDGE WOOD LAKE
4th rowNORTHRIDGE WOOD LAKE
5th rowNORTHRIDGE WOOD LAKE

Common Values

ValueCountFrequency (%)
LANDMARK ON THE LAKE 27
 
0.4%
WOODLANDS 26
 
0.4%
BLATZ 23
 
0.3%
MILL VALLEY 20
 
0.3%
POINT ON THE RIVER CONDOS 19
 
0.3%
RIVER RENAISSANCE 17
 
0.2%
601 LOFTS 17
 
0.2%
RIVERBRIDGE 16
 
0.2%
SERVITE WOODS (ATRIUM) 15
 
0.2%
WILLOW CREEK 14
 
0.2%
Other values (192) 691
 
9.7%
(Missing) 6261
87.6%

Length

2024-03-01T10:49:36.769060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
condominium 100
 
4.6%
on 89
 
4.1%
lofts 77
 
3.5%
the 70
 
3.2%
lake 66
 
3.0%
river 58
 
2.7%
condos 55
 
2.5%
condominiums 55
 
2.5%
terrace 39
 
1.8%
point 32
 
1.5%
Other values (250) 1538
70.6%

Most occurring characters

ValueCountFrequency (%)
E 1391
 
9.1%
1353
 
8.9%
O 1352
 
8.8%
R 1153
 
7.5%
N 1055
 
6.9%
I 1006
 
6.6%
A 960
 
6.3%
T 796
 
5.2%
S 795
 
5.2%
L 757
 
5.0%
Other values (37) 4664
30.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13616
89.1%
Space Separator 1353
 
8.9%
Decimal Number 128
 
0.8%
Close Punctuation 65
 
0.4%
Open Punctuation 65
 
0.4%
Dash Punctuation 39
 
0.3%
Other Punctuation 9
 
0.1%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1391
 
10.2%
O 1352
 
9.9%
R 1153
 
8.5%
N 1055
 
7.7%
I 1006
 
7.4%
A 960
 
7.1%
T 796
 
5.8%
S 795
 
5.8%
L 757
 
5.6%
D 599
 
4.4%
Other values (16) 3752
27.6%
Decimal Number
ValueCountFrequency (%)
1 37
28.9%
2 30
23.4%
0 20
15.6%
5 18
14.1%
6 18
14.1%
3 2
 
1.6%
4 1
 
0.8%
8 1
 
0.8%
7 1
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
e 2
28.6%
a 2
28.6%
n 1
14.3%
l 1
14.3%
c 1
14.3%
Other Punctuation
ValueCountFrequency (%)
& 4
44.4%
/ 3
33.3%
' 2
22.2%
Space Separator
ValueCountFrequency (%)
1353
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13623
89.1%
Common 1659
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1391
 
10.2%
O 1352
 
9.9%
R 1153
 
8.5%
N 1055
 
7.7%
I 1006
 
7.4%
A 960
 
7.0%
T 796
 
5.8%
S 795
 
5.8%
L 757
 
5.6%
D 599
 
4.4%
Other values (21) 3759
27.6%
Common
ValueCountFrequency (%)
1353
81.6%
) 65
 
3.9%
( 65
 
3.9%
- 39
 
2.4%
1 37
 
2.2%
2 30
 
1.8%
0 20
 
1.2%
5 18
 
1.1%
6 18
 
1.1%
& 4
 
0.2%
Other values (6) 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1391
 
9.1%
1353
 
8.9%
O 1352
 
8.8%
R 1153
 
7.5%
N 1055
 
6.9%
I 1006
 
6.6%
A 960
 
6.3%
T 796
 
5.2%
S 795
 
5.2%
L 757
 
5.0%
Other values (37) 4664
30.5%

District
Real number (ℝ)

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8376714
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:37.284474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile14
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2621201
Coefficient of variation (CV)0.54379929
Kurtosis-1.2297748
Mean7.8376714
Median Absolute Deviation (MAD)3
Skewness0.01487817
Sum56008
Variance18.165668
MonotonicityNot monotonic
2024-03-01T10:49:37.674905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 778
10.9%
11 612
 
8.6%
10 586
 
8.2%
14 562
 
7.9%
2 531
 
7.4%
7 530
 
7.4%
13 524
 
7.3%
3 513
 
7.2%
9 488
 
6.8%
1 468
 
6.5%
Other values (5) 1554
21.7%
ValueCountFrequency (%)
1 468
6.5%
2 531
7.4%
3 513
7.2%
4 322
4.5%
5 778
10.9%
6 366
5.1%
7 530
7.4%
8 273
 
3.8%
9 488
6.8%
10 586
8.2%
ValueCountFrequency (%)
15 297
4.2%
14 562
7.9%
13 524
7.3%
12 296
4.1%
11 612
8.6%
10 586
8.2%
9 488
6.8%
8 273
3.8%
7 530
7.4%
6 366
5.1%

nbhd
Real number (ℝ)

Distinct459
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3338.4563
Minimum40
Maximum24910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:38.234945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile780
Q11780
median3060
Q34620
95-th percentile6277
Maximum24910
Range24870
Interquartile range (IQR)2840

Descriptive statistics

Standard deviation1795.1757
Coefficient of variation (CV)0.53772626
Kurtosis1.7286574
Mean3338.4563
Median Absolute Deviation (MAD)1520
Skewness0.36972966
Sum23856609
Variance3222655.6
MonotonicityNot monotonic
2024-03-01T10:49:38.815678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 156
 
2.2%
2080 132
 
1.8%
4520 124
 
1.7%
4120 119
 
1.7%
1140 118
 
1.7%
4420 101
 
1.4%
4240 99
 
1.4%
4340 98
 
1.4%
4620 93
 
1.3%
1440 92
 
1.3%
Other values (449) 6014
84.2%
ValueCountFrequency (%)
40 14
 
0.2%
50 4
 
0.1%
240 61
0.9%
360 31
0.4%
380 14
 
0.2%
440 43
0.6%
480 71
1.0%
520 8
 
0.1%
560 41
0.6%
600 22
 
0.3%
ValueCountFrequency (%)
24910 1
 
< 0.1%
6982 1
 
< 0.1%
6981 1
 
< 0.1%
6980 1
 
< 0.1%
6979 1
 
< 0.1%
6978 1
 
< 0.1%
6977 2
< 0.1%
6976 1
 
< 0.1%
6974 4
0.1%
6973 1
 
< 0.1%

Style
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct81
Distinct (%)1.1%
Missing21
Missing (%)0.3%
Memory size484.5 KiB
Ranch
1495 
Cape Cod
1006 
Duplex O/S
552 
Milwaukee Bungalow
458 
Res O/S A & 1/2
439 
Other values (76)
3175 

Length

Max length50
Median length48
Mean length12.516491
Min length5

Characters and Unicode

Total characters89180
Distinct characters63
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st rowService Building
2nd rowOffice Building - 1 Story
3rd rowRanch
4th rowRanch
5th rowRanch

Common Values

ValueCountFrequency (%)
Ranch 1495
20.9%
Cape Cod 1006
14.1%
Duplex O/S 552
 
7.7%
Milwaukee Bungalow 458
 
6.4%
Res O/S A & 1/2 439
 
6.1%
Dplx Bungalow 413
 
5.8%
Duplex N/S 381
 
5.3%
Mid Rise 4-12 Stories 339
 
4.7%
Colonial 285
 
4.0%
Low Rise 1-3 Stories 244
 
3.4%
Other values (71) 1513
21.2%

Length

2024-03-01T10:49:39.403396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ranch 1507
 
8.8%
o/s 1174
 
6.8%
cod 1006
 
5.9%
cape 1006
 
5.9%
999
 
5.8%
duplex 954
 
5.6%
bungalow 871
 
5.1%
rise 681
 
4.0%
stories 681
 
4.0%
res 628
 
3.7%
Other values (146) 7656
44.6%

Most occurring characters

ValueCountFrequency (%)
10040
 
11.3%
e 6566
 
7.4%
o 5544
 
6.2%
a 4859
 
5.4%
n 4344
 
4.9%
l 4294
 
4.8%
i 3979
 
4.5%
t 3052
 
3.4%
C 3005
 
3.4%
p 2981
 
3.3%
Other values (53) 40516
45.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54926
61.6%
Uppercase Letter 16158
 
18.1%
Space Separator 10040
 
11.3%
Decimal Number 3547
 
4.0%
Other Punctuation 2687
 
3.0%
Dash Punctuation 1017
 
1.1%
Open Punctuation 303
 
0.3%
Math Symbol 260
 
0.3%
Close Punctuation 242
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6566
12.0%
o 5544
10.1%
a 4859
 
8.8%
n 4344
 
7.9%
l 4294
 
7.8%
i 3979
 
7.2%
t 3052
 
5.6%
p 2981
 
5.4%
u 2972
 
5.4%
s 2913
 
5.3%
Other values (15) 13422
24.4%
Uppercase Letter
ValueCountFrequency (%)
C 3005
18.6%
R 2926
18.1%
S 2678
16.6%
D 1450
9.0%
O 1283
7.9%
B 1115
 
6.9%
A 993
 
6.1%
M 988
 
6.1%
T 387
 
2.4%
N 384
 
2.4%
Other values (10) 949
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 1449
40.9%
2 1114
31.4%
4 509
 
14.4%
3 257
 
7.2%
6 161
 
4.5%
7 41
 
1.2%
0 13
 
0.4%
8 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 2002
74.5%
& 589
 
21.9%
, 88
 
3.3%
. 8
 
0.3%
Math Symbol
ValueCountFrequency (%)
+ 162
62.3%
> 98
37.7%
Space Separator
ValueCountFrequency (%)
10040
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1017
100.0%
Open Punctuation
ValueCountFrequency (%)
( 303
100.0%
Close Punctuation
ValueCountFrequency (%)
) 242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71084
79.7%
Common 18096
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6566
 
9.2%
o 5544
 
7.8%
a 4859
 
6.8%
n 4344
 
6.1%
l 4294
 
6.0%
i 3979
 
5.6%
t 3052
 
4.3%
C 3005
 
4.2%
p 2981
 
4.2%
u 2972
 
4.2%
Other values (35) 29488
41.5%
Common
ValueCountFrequency (%)
10040
55.5%
/ 2002
 
11.1%
1 1449
 
8.0%
2 1114
 
6.2%
- 1017
 
5.6%
& 589
 
3.3%
4 509
 
2.8%
( 303
 
1.7%
3 257
 
1.4%
) 242
 
1.3%
Other values (8) 574
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10040
 
11.3%
e 6566
 
7.4%
o 5544
 
6.2%
a 4859
 
5.4%
n 4344
 
4.9%
l 4294
 
4.8%
i 3979
 
4.5%
t 3052
 
3.4%
C 3005
 
3.4%
p 2981
 
3.3%
Other values (53) 40516
45.4%

Extwall
Categorical

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)0.3%
Missing926
Missing (%)13.0%
Memory size442.2 KiB
Aluminum/Vinyl
3468 
Brick
1408 
Wood
 
331
Asphalt/Other
 
313
Stone
 
179
Other values (13)
521 

Length

Max length23
Median length14
Mean length11.009646
Min length4

Characters and Unicode

Total characters68480
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowConcrete Block
2nd rowBrick
3rd rowAluminum/Vinyl
4th rowAluminum/Vinyl
5th rowAluminum/Vinyl

Common Values

ValueCountFrequency (%)
Aluminum/Vinyl 3468
48.5%
Brick 1408
19.7%
Wood 331
 
4.6%
Asphalt/Other 313
 
4.4%
Stone 179
 
2.5%
Masonry/Frame 154
 
2.2%
Stucco 93
 
1.3%
Concrete Block 77
 
1.1%
Fiber Cement/Hardiplank 49
 
0.7%
Alum/Vynyl Siding 46
 
0.6%
Other values (8) 102
 
1.4%
(Missing) 926
 
13.0%

Length

2024-03-01T10:49:40.009509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aluminum/vinyl 3468
53.7%
brick 1410
21.8%
wood 342
 
5.3%
asphalt/other 313
 
4.8%
stone 179
 
2.8%
masonry/frame 154
 
2.4%
block 104
 
1.6%
stucco 93
 
1.4%
concrete 77
 
1.2%
siding 58
 
0.9%
Other values (10) 262
 
4.1%

Most occurring characters

ValueCountFrequency (%)
i 8560
12.5%
n 7591
11.1%
l 7506
11.0%
m 7224
10.5%
u 7075
10.3%
/ 4030
 
5.9%
A 3827
 
5.6%
y 3755
 
5.5%
V 3514
 
5.1%
r 2310
 
3.4%
Other values (22) 13088
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53722
78.4%
Uppercase Letter 10488
 
15.3%
Other Punctuation 4030
 
5.9%
Space Separator 240
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 8560
15.9%
n 7591
14.1%
l 7506
14.0%
m 7224
13.4%
u 7075
13.2%
y 3755
7.0%
r 2310
 
4.3%
c 1791
 
3.3%
k 1563
 
2.9%
o 1334
 
2.5%
Other values (9) 5013
9.3%
Uppercase Letter
ValueCountFrequency (%)
A 3827
36.5%
V 3514
33.5%
B 1514
 
14.4%
W 342
 
3.3%
S 330
 
3.1%
O 323
 
3.1%
F 231
 
2.2%
M 207
 
2.0%
C 126
 
1.2%
H 49
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/ 4030
100.0%
Space Separator
ValueCountFrequency (%)
240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64210
93.8%
Common 4270
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 8560
13.3%
n 7591
11.8%
l 7506
11.7%
m 7224
11.3%
u 7075
11.0%
A 3827
 
6.0%
y 3755
 
5.8%
V 3514
 
5.5%
r 2310
 
3.6%
c 1791
 
2.8%
Other values (20) 11057
17.2%
Common
ValueCountFrequency (%)
/ 4030
94.4%
240
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 8560
12.5%
n 7591
11.1%
l 7506
11.0%
m 7224
10.5%
u 7075
10.3%
/ 4030
 
5.9%
A 3827
 
5.6%
y 3755
 
5.5%
V 3514
 
5.1%
r 2310
 
3.4%
Other values (22) 13088
19.1%

Stories
Real number (ℝ)

Distinct13
Distinct (%)0.2%
Missing39
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.3848319
Minimum0
Maximum14
Zeros21
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:40.496235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5348119
Coefficient of variation (CV)0.38619266
Kurtosis69.638741
Mean1.3848319
Median Absolute Deviation (MAD)0
Skewness4.044354
Sum9842
Variance0.28602376
MonotonicityNot monotonic
2024-03-01T10:49:40.920694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 3955
55.3%
2 2008
28.1%
1.5 1015
 
14.2%
3 52
 
0.7%
2.5 40
 
0.6%
0 21
 
0.3%
4 7
 
0.1%
5 3
 
< 0.1%
7 2
 
< 0.1%
12 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 39
 
0.5%
ValueCountFrequency (%)
0 21
 
0.3%
1 3955
55.3%
1.5 1015
 
14.2%
2 2008
28.1%
2.5 40
 
0.6%
3 52
 
0.7%
3.5 1
 
< 0.1%
4 7
 
0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 1
 
< 0.1%
7 2
 
< 0.1%
6 1
 
< 0.1%
5 3
 
< 0.1%
4 7
 
0.1%
3.5 1
 
< 0.1%
3 52
 
0.7%
2.5 40
 
0.6%
2 2008
28.1%

Year_Built
Real number (ℝ)

Distinct155
Distinct (%)2.2%
Missing11
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1936.1706
Minimum0
Maximum2022
Zeros20
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:41.538664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1892
Q11921
median1948
Q31958
95-th percentile1999
Maximum2022
Range2022
Interquartile range (IQR)37

Descriptive statistics

Standard deviation106.7051
Coefficient of variation (CV)0.055111416
Kurtosis301.17114
Mean1936.1706
Median Absolute Deviation (MAD)20
Skewness-16.740934
Sum13814577
Variance11385.979
MonotonicityNot monotonic
2024-03-01T10:49:42.150833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1955 249
 
3.5%
1952 212
 
3.0%
1951 196
 
2.7%
1954 195
 
2.7%
1953 193
 
2.7%
1956 183
 
2.6%
1950 178
 
2.5%
1957 177
 
2.5%
1958 168
 
2.4%
1926 138
 
1.9%
Other values (145) 5246
73.4%
ValueCountFrequency (%)
0 20
0.3%
1836 1
 
< 0.1%
1843 1
 
< 0.1%
1855 2
 
< 0.1%
1860 2
 
< 0.1%
1861 2
 
< 0.1%
1865 3
 
< 0.1%
1868 1
 
< 0.1%
1869 1
 
< 0.1%
1870 15
0.2%
ValueCountFrequency (%)
2022 6
0.1%
2020 1
 
< 0.1%
2019 1
 
< 0.1%
2018 3
< 0.1%
2017 2
 
< 0.1%
2016 4
0.1%
2015 1
 
< 0.1%
2014 2
 
< 0.1%
2013 3
< 0.1%
2012 1
 
< 0.1%

Rooms
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct40
Distinct (%)0.6%
Missing443
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean7.7193794
Minimum0
Maximum63
Zeros122
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:42.811916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median7
Q310
95-th percentile14.9
Maximum63
Range63
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1556761
Coefficient of variation (CV)0.53834329
Kurtosis15.325323
Mean7.7193794
Median Absolute Deviation (MAD)2
Skewness2.4306361
Sum51743
Variance17.269644
MonotonicityNot monotonic
2024-03-01T10:49:43.362981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 1427
20.0%
6 991
13.9%
10 905
12.7%
4 644
9.0%
8 573
8.0%
7 512
 
7.2%
12 410
 
5.7%
9 305
 
4.3%
14 141
 
2.0%
11 137
 
1.9%
Other values (30) 658
9.2%
(Missing) 443
 
6.2%
ValueCountFrequency (%)
0 122
 
1.7%
1 6
 
0.1%
2 21
 
0.3%
3 128
 
1.8%
4 644
9.0%
5 1427
20.0%
6 991
13.9%
7 512
 
7.2%
8 573
8.0%
9 305
 
4.3%
ValueCountFrequency (%)
63 1
 
< 0.1%
62 1
 
< 0.1%
45 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 2
 
< 0.1%
30 8
0.1%

FinishedSqft
Real number (ℝ)

Distinct2386
Distinct (%)33.5%
Missing24
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2334.2627
Minimum0
Maximum245266
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:43.962255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile787
Q11082
median1402.5
Q32014
95-th percentile3672
Maximum245266
Range245266
Interquartile range (IQR)932

Descriptive statistics

Standard deviation8425.9847
Coefficient of variation (CV)3.6096986
Kurtosis406.49186
Mean2334.2627
Median Absolute Deviation (MAD)397.5
Skewness18.519566
Sum16624619
Variance70997219
MonotonicityNot monotonic
2024-03-01T10:49:44.575291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1056 30
 
0.4%
936 30
 
0.4%
864 29
 
0.4%
1120 26
 
0.4%
672 26
 
0.4%
1054 25
 
0.3%
1140 23
 
0.3%
2040 22
 
0.3%
980 21
 
0.3%
912 18
 
0.3%
Other values (2376) 6872
96.2%
(Missing) 24
 
0.3%
ValueCountFrequency (%)
0 6
0.1%
325 4
0.1%
405 2
 
< 0.1%
430 2
 
< 0.1%
460 3
< 0.1%
476 1
 
< 0.1%
496 1
 
< 0.1%
498 2
 
< 0.1%
500 1
 
< 0.1%
508 1
 
< 0.1%
ValueCountFrequency (%)
245266 1
< 0.1%
232960 1
< 0.1%
210744 1
< 0.1%
202568 1
< 0.1%
196753 1
< 0.1%
170090 1
< 0.1%
156025 1
< 0.1%
141787 1
< 0.1%
139280 1
< 0.1%
127812 1
< 0.1%

Units
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct49
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0100756
Minimum0
Maximum737
Zeros29
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:45.560316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum737
Range737
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.166496
Coefficient of variation (CV)7.047743
Kurtosis2070.7177
Mean2.0100756
Median Absolute Deviation (MAD)0
Skewness42.806223
Sum14364
Variance200.68961
MonotonicityNot monotonic
2024-03-01T10:49:46.164049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 5070
70.9%
2 1564
 
21.9%
4 166
 
2.3%
3 135
 
1.9%
8 40
 
0.6%
0 29
 
0.4%
6 28
 
0.4%
5 25
 
0.3%
7 16
 
0.2%
12 8
 
0.1%
Other values (39) 65
 
0.9%
ValueCountFrequency (%)
0 29
 
0.4%
1 5070
70.9%
2 1564
 
21.9%
3 135
 
1.9%
4 166
 
2.3%
5 25
 
0.3%
6 28
 
0.4%
7 16
 
0.2%
8 40
 
0.6%
9 5
 
0.1%
ValueCountFrequency (%)
737 1
< 0.1%
725 1
< 0.1%
389 1
< 0.1%
300 1
< 0.1%
116 1
< 0.1%
115 1
< 0.1%
101 1
< 0.1%
99 1
< 0.1%
94 2
< 0.1%
84 1
< 0.1%

Bdrms
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)0.4%
Missing443
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean3.9258541
Minimum0
Maximum32
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:46.725625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q35
95-th percentile8
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0797352
Coefficient of variation (CV)0.52975355
Kurtosis17.125512
Mean3.9258541
Median Absolute Deviation (MAD)1
Skewness2.6006406
Sum26315
Variance4.3252983
MonotonicityNot monotonic
2024-03-01T10:49:47.227169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 2200
30.8%
4 1484
20.8%
2 1037
14.5%
6 869
 
12.2%
5 371
 
5.2%
1 242
 
3.4%
8 215
 
3.0%
7 90
 
1.3%
10 53
 
0.7%
12 44
 
0.6%
Other values (14) 98
 
1.4%
(Missing) 443
 
6.2%
ValueCountFrequency (%)
0 18
 
0.3%
1 242
 
3.4%
2 1037
14.5%
3 2200
30.8%
4 1484
20.8%
5 371
 
5.2%
6 869
 
12.2%
7 90
 
1.3%
8 215
 
3.0%
9 38
 
0.5%
ValueCountFrequency (%)
32 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
25 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
18 4
 
0.1%
16 1
 
< 0.1%
15 4
 
0.1%
14 10
0.1%

Fbath
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4354884
Minimum0
Maximum7
Zeros509
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:47.664394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.71871514
Coefficient of variation (CV)0.50067639
Kurtosis1.2512878
Mean1.4354884
Median Absolute Deviation (MAD)1
Skewness0.36124013
Sum10258
Variance0.51655145
MonotonicityNot monotonic
2024-03-01T10:49:48.073456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3409
47.7%
2 2885
40.4%
0 509
 
7.1%
3 304
 
4.3%
4 31
 
0.4%
5 6
 
0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 509
 
7.1%
1 3409
47.7%
2 2885
40.4%
3 304
 
4.3%
4 31
 
0.4%
5 6
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 1
 
< 0.1%
5 6
 
0.1%
4 31
 
0.4%
3 304
 
4.3%
2 2885
40.4%
1 3409
47.7%
0 509
 
7.1%

Hbath
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
0
5183 
1
1793 
2
 
164
3
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7146
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Length

2024-03-01T10:49:48.586989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T10:49:49.163755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7146
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7146
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Lotsize
Real number (ℝ)

SKEWED  ZEROS 

Distinct1670
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6676.4801
Minimum0
Maximum1341648
Zeros489
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:49.690576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13660
median5002
Q36750
95-th percentile11314.75
Maximum1341648
Range1341648
Interquartile range (IQR)3090

Descriptive statistics

Standard deviation24988.764
Coefficient of variation (CV)3.7428051
Kurtosis1483.0128
Mean6676.4801
Median Absolute Deviation (MAD)1402
Skewness33.886178
Sum47710127
Variance6.2443833 × 108
MonotonicityNot monotonic
2024-03-01T10:49:50.334844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 489
 
6.8%
1 456
 
6.4%
4800 419
 
5.9%
3600 341
 
4.8%
6000 171
 
2.4%
5400 136
 
1.9%
7200 135
 
1.9%
5000 117
 
1.6%
4920 95
 
1.3%
4200 92
 
1.3%
Other values (1660) 4695
65.7%
ValueCountFrequency (%)
0 489
6.8%
1 456
6.4%
75 1
 
< 0.1%
613 1
 
< 0.1%
929 1
 
< 0.1%
1050 1
 
< 0.1%
1080 1
 
< 0.1%
1084 1
 
< 0.1%
1098 1
 
< 0.1%
1120 1
 
< 0.1%
ValueCountFrequency (%)
1341648 1
< 0.1%
835916 1
< 0.1%
788775 1
< 0.1%
429109 1
< 0.1%
409333 1
< 0.1%
388990 1
< 0.1%
306096 1
< 0.1%
277825 1
< 0.1%
261360 1
< 0.1%
243848 1
< 0.1%

Sale_date
Categorical

Distinct313
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size467.7 KiB
2022-02-28
 
64
2022-06-30
 
63
2022-03-18
 
54
2022-02-04
 
50
2022-04-28
 
50
Other values (308)
6865 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters71460
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.5%

Sample

1st row2022-04-01
2nd row2022-10-07
3rd row2022-01-07
4th row2022-08-09
5th row2022-05-23

Common Values

ValueCountFrequency (%)
2022-02-28 64
 
0.9%
2022-06-30 63
 
0.9%
2022-03-18 54
 
0.8%
2022-02-04 50
 
0.7%
2022-04-28 50
 
0.7%
2022-06-17 50
 
0.7%
2022-04-29 49
 
0.7%
2022-05-26 49
 
0.7%
2022-05-31 49
 
0.7%
2022-09-30 48
 
0.7%
Other values (303) 6620
92.6%

Length

2024-03-01T10:49:50.893235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-02-28 64
 
0.9%
2022-06-30 63
 
0.9%
2022-03-18 54
 
0.8%
2022-04-28 50
 
0.7%
2022-06-17 50
 
0.7%
2022-02-04 50
 
0.7%
2022-04-29 49
 
0.7%
2022-05-26 49
 
0.7%
2022-05-31 49
 
0.7%
2022-09-30 48
 
0.7%
Other values (303) 6620
92.6%

Most occurring characters

ValueCountFrequency (%)
2 25466
35.6%
0 15966
22.3%
- 14292
20.0%
1 5582
 
7.8%
3 1712
 
2.4%
8 1598
 
2.2%
4 1440
 
2.0%
6 1424
 
2.0%
5 1379
 
1.9%
7 1349
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57168
80.0%
Dash Punctuation 14292
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 25466
44.5%
0 15966
27.9%
1 5582
 
9.8%
3 1712
 
3.0%
8 1598
 
2.8%
4 1440
 
2.5%
6 1424
 
2.5%
5 1379
 
2.4%
7 1349
 
2.4%
9 1252
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 14292
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 25466
35.6%
0 15966
22.3%
- 14292
20.0%
1 5582
 
7.8%
3 1712
 
2.4%
8 1598
 
2.2%
4 1440
 
2.0%
6 1424
 
2.0%
5 1379
 
1.9%
7 1349
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 25466
35.6%
0 15966
22.3%
- 14292
20.0%
1 5582
 
7.8%
3 1712
 
2.4%
8 1598
 
2.2%
4 1440
 
2.0%
6 1424
 
2.0%
5 1379
 
1.9%
7 1349
 
1.9%

Sale_price
Real number (ℝ)

Distinct1284
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271544.97
Minimum4000
Maximum21850000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-03-01T10:49:51.433252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile64000
Q1131000
median195000
Q3260000
95-th percentile481720
Maximum21850000
Range21846000
Interquartile range (IQR)129000

Descriptive statistics

Standard deviation770141.28
Coefficient of variation (CV)2.8361464
Kurtosis399.43511
Mean271544.97
Median Absolute Deviation (MAD)65000
Skewness18.427415
Sum1.9404603 × 109
Variance5.931176 × 1011
MonotonicityNot monotonic
2024-03-01T10:49:52.057804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 117
 
1.6%
250000 117
 
1.6%
220000 104
 
1.5%
160000 101
 
1.4%
225000 99
 
1.4%
150000 95
 
1.3%
180000 94
 
1.3%
190000 93
 
1.3%
175000 91
 
1.3%
210000 90
 
1.3%
Other values (1274) 6145
86.0%
ValueCountFrequency (%)
4000 1
< 0.1%
5000 1
< 0.1%
7000 1
< 0.1%
9000 1
< 0.1%
10000 2
< 0.1%
11000 1
< 0.1%
12500 1
< 0.1%
15000 2
< 0.1%
16000 1
< 0.1%
18000 1
< 0.1%
ValueCountFrequency (%)
21850000 1
< 0.1%
20828000 1
< 0.1%
20000000 1
< 0.1%
17400000 1
< 0.1%
17225000 1
< 0.1%
14600000 2
< 0.1%
14500000 1
< 0.1%
14450000 1
< 0.1%
14250000 1
< 0.1%
13735000 1
< 0.1%

Interactions

2024-03-01T10:49:21.886226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:45.814715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:49.551793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:53.197960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:56.960402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:59.969107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:03.100176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:06.144958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:08.401586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:11.230245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:13.813434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:16.200571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:18.851021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:22.526539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:46.017689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:49.846685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:53.440560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:57.339502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:00.179135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:03.356745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:06.305295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:08.628054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:11.373826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:13.975899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:16.428253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:19.034316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:23.067373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:46.261881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:50.100964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:53.749911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:57.682161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:00.399717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:03.634961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:06.455195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:08.889396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:11.577328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:14.135938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:16.701988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:19.209795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:23.521807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:46.523995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:50.330120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:54.005617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:57.991373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:00.602979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:03.828313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:06.637302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:09.113082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:11.703039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:14.297966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:16.875094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:19.408230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:23.984828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:46.782027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:50.571077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:54.198441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:58.196665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:00.829760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:04.077575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:06.799346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:09.352644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:11.871089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:14.433972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:17.085634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:19.514165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:24.449737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:47.046264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:50.813054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:54.382864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:58.427753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:01.061321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:04.275404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:06.908414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:09.596295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:12.084292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:14.626835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:17.305984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:19.699056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:25.049857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:47.500828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:51.048157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:54.574277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:58.624956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:01.346691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:04.538661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:07.082832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:09.859787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:12.350092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:14.785784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:17.459561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:19.852558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:25.568320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:47.878445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:51.292639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:54.788238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:58.809702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:01.593660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:04.746530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:07.225990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:10.058340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:12.603890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:14.942775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:17.692185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:20.040940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:26.121384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:48.143399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:51.593445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:54.955984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:59.009491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:01.805486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:04.983767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:07.447675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:10.265996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:12.826688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:15.071734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:17.863330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:20.292746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:26.645169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:48.569363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:51.868345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:55.332199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:59.214581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:02.222372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:05.272661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:07.725263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:10.537123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:13.229018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:15.295806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:18.083259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:20.558965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:27.178100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:48.840926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:52.177957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:55.734367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:59.403026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:02.475558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:05.501936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:07.911211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:10.758128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:13.380836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:15.490232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:18.315121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:20.738910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:27.664944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:49.122943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:52.500285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:56.203118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:59.570429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:02.717652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:05.744735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:08.060888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:10.913306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:13.531907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:15.798084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:18.482449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:20.956976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:28.158027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:49.379422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:52.869724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:56.607291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:48:59.767050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:02.878579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:05.950176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:08.202670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:11.075346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:13.675810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:15.948538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:18.695831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-01T10:49:21.423595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-03-01T10:49:52.630154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
PropertyIDtaxkeyDistrictnbhdStoriesYear_BuiltRoomsFinishedSqftUnitsBdrmsFbathLotsizeSale_pricePropTypeStyleExtwallHbath
PropertyID1.0000.9850.6600.6310.014-0.2120.0200.0260.028-0.0120.021-0.1110.3190.1900.3110.1080.152
taxkey0.9851.0000.6650.6280.014-0.2140.0200.0170.024-0.0120.031-0.1220.3200.1900.3190.1080.167
District0.6600.6651.0000.3760.063-0.1270.0990.0410.0300.0850.0090.0090.0890.1870.3010.1000.134
nbhd0.6310.6280.3761.0000.1130.006-0.1940.0900.110-0.236-0.198-0.3410.3590.8140.7340.4940.046
Stories0.0140.0140.0630.1131.000-0.1880.4380.6570.6190.3740.278-0.0580.1120.1640.6750.1280.065
Year_Built-0.212-0.214-0.1270.006-0.1881.000-0.306-0.235-0.247-0.220-0.1500.2380.1690.0320.9951.0000.025
Rooms0.0200.0200.099-0.1940.438-0.3061.0000.6050.5060.8680.4810.0940.0300.2000.2580.0410.079
FinishedSqft0.0260.0170.0410.0900.657-0.2350.6051.0000.6820.5480.3360.1110.3300.1930.5390.2040.000
Units0.0280.0240.0300.1100.619-0.2470.5060.6821.0000.4470.2520.0440.0450.0920.7570.2120.000
Bdrms-0.012-0.0120.085-0.2360.374-0.2200.8680.5480.4471.0000.3800.2080.0330.1840.2080.0190.078
Fbath0.0210.0310.009-0.1980.278-0.1500.4810.3360.2520.3801.000-0.1280.0480.4340.4990.2630.189
Lotsize-0.111-0.1220.009-0.341-0.0580.2380.0940.1110.0440.208-0.1281.0000.1960.1000.3730.2080.000
Sale_price0.3190.3200.0890.3590.1120.1690.0300.3300.0450.0330.0480.1961.0000.2160.5390.1890.000
PropType0.1900.1900.1870.8140.1640.0320.2000.1930.0920.1840.4340.1000.2161.0000.8830.4960.099
Style0.3110.3190.3010.7340.6750.9950.2580.5390.7570.2080.4990.3730.5390.8831.0000.4010.409
Extwall0.1080.1080.1000.4940.1281.0000.0410.2040.2120.0190.2630.2080.1890.4960.4011.0000.096
Hbath0.1520.1670.1340.0460.0650.0250.0790.0000.0000.0780.1890.0000.0000.0990.4090.0961.000

Missing values

2024-03-01T10:49:29.116001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-01T10:49:30.686594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-01T10:49:31.704036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PropertyIDPropTypetaxkeyAddressCondoProjectDistrictnbhdStyleExtwallStoriesYear_BuiltRoomsFinishedSqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_price
098461Manufacturing301310009434-9446 N 107TH STNaN96300Service BuildingConcrete Block1.01978.0NaN20600.06NaN0002022-04-01950000.0
198464Commercial301520009306-9316 N 107TH STNaN96202Office Building - 1 StoryBrick1.01982.0NaN9688.023NaN00357192022-10-07385000.0
298508Residential499801109327 N SWAN RDNaN940NaNNaNNaNNaNNaNNaN0NaN0013416482022-01-07800000.0
398519Residential499932009411 W COUNTY LINE RDNaN940RanchAluminum/Vinyl1.01959.06.01334.013.011832002022-08-09280000.0
498561Residential500420009322 N JOYCE AVNaN940RanchAluminum/Vinyl1.01980.010.01006.016.01083032022-05-23233100.0
598593Residential500740009360 N 85TH STNaN940RanchAluminum/Vinyl1.01982.05.01007.013.01072002022-07-25215000.0
698604Residential500850009305 N BURBANK AVNaN940RanchAluminum/Vinyl1.01984.05.01301.013.02072002022-03-29150000.0
798608Residential500890009217 N 83RD STNaN940ColonialAluminum/Vinyl2.02007.09.02237.014.021156772022-05-10400000.0
898696Condominium700170009192 N 70TH ST, Unit 2NORTHRIDGE WOOD LAKE95010Condo TownhouseNaN2.01973.07.01437.013.02102022-05-16122000.0
998715Condominium700360009212 N 70TH ST, Unit 8NORTHRIDGE WOOD LAKE95010Condo TownhouseNaN2.01973.07.01437.014.02102022-04-14123000.0
PropertyIDPropTypetaxkeyAddressCondoProjectDistrictnbhdStyleExtwallStoriesYear_BuiltRoomsFinishedSqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_price
7136260546Residential71602410001821 W SALEM STNaN134920RanchAluminum/Vinyl1.01960.05.0965.013.01060002022-07-22220000.0
7137260559Residential71602540006507 S 17TH STNaN134920RanchAluminum/Vinyl1.01960.05.01060.013.01160482022-08-22240000.0
7138260584Residential71602790006444 S 18TH STNaN134920RanchAluminum/Vinyl1.01961.05.0982.013.01070002022-08-30195000.0
7139260588Residential71602830006465 S 18TH STNaN134920RanchAluminum/Vinyl1.01960.010.0965.016.01060932022-10-12260000.0
7140260630Condominium71603270001928 W SALEM STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN1.01974.05.01141.012.02012022-11-21159900.0
7141260642Condominium71603390001912 W SALEM STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN2.01974.010.01100.014.01112022-03-11125900.0
7142260654Condominium71603510006316 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN1.01974.05.01379.012.01112022-10-28150000.0
7143260668Condominium71603650006376 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN2.01974.010.01100.014.01112022-03-15130000.0
7144260669Condominium71603660006378 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN2.01974.05.01100.012.01112022-12-30123000.0
7145260678Condominium71603750006354 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN1.01974.05.01141.012.01112022-07-08157500.0